Simultaneous Localization and
Map Building (SLAM) and Map Aided Localization (MAL) are very effective
techniques employed extensively in robot navigation tasks. However, biases
and drifts in both exteroceptive and proprioceptive sensors adversely impair
correct localization (in MAL) and also impair map building (in SLAM). More
specifically, accumulated errors as a result of biases in the sensors cause
the algorithms to diverge and produce inconsistent and inaccurate results.
Although offline calibration of these sensors can reduce the effects to
some extent, the process results in longer setup and processing times. Moreover,
during operation, the sensors' calibration may often be subject to
changes or drifts requiring regular resetting and initialization. A convenient,
appropriate and effective approach to overcome problems associated with
biases in sensors has been to explicitly model and estimate the bias parameters
concurrently with the vehicle state online using an augmented state space
approach. This paper investigates the properties of the concurrent bias
estimation in MAL using an augmented, estimation theoretic state space approach
for the localization of a large class of mobile robots, consisting of autonomous
ground vehicles. This involves a rigorous theoretical study of the issues
of observability and convergence, their interrelations and effects on the
algorithm's performance. This paper shows analytically that if sensor
biases are estimated jointly with the vehicle pose in a MAL framework: 1)
The uncertainties of the estimated errors in the bias parameters of both
proprioceptive and exteroceptive sensors diminish in each update. 2) A derived
lower bound is reached in each of these estimates. 3) The rate of convergence
to this lower bound is also derived. 4) Although often neglected in the
literature, observability is a major issue. From the analysis it is derived
that in order to guarantee observability in MAL with bias estimation, it
is necessary to observe simultaneouslyat least two distinct landmarks, which
are not on a straight line with the vehicle position. Extensive simulations
are provided to illustrate the theoretical results established for the general
case of nonlinear dynamics and slowly varying sensor biases. The results
are further exemplified and verified experimentally using a sophisticated
MAL algorithm, utilizing a low cost inertial navigation sensor suite.